Educational data mining and learning analytics
By V. Hernández, J. Carles. 2017
589 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/dbd9c0c7888fc46de6bc917c2056abf325bc30c2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Cristóbal Romero
- Sebastián Ventura
- Dragan Gasevic
- Ryan Shaun Joazeiro de Baker
- R. Luckin
- Shane Dawson
- Mutlu Cukurova
- Z. Pardos
Citations in the corpus
- Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, background): “Thus, it is not surprising that the insights gleaned from research on student academic risk are being so readily and eagerly adopted across the sector (Siemens et al., 2014).”
- Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help? (2016, unknown): “One promising approach for making structuring more appropriate to particular groups may come in the form of appropriate learning analytics, based on the big data generated in many CSCL environments (Baker & Siemens, 2014).”
- Equity Beyond Bias in Language Technologies for Education (2019, background): “learning analytics, tying educational data mining to specific student outcomes (Baker and Inventado, 2014).”
- Mining Big Data in Education: Affordances and Challenges (2020, methodology): “Given the fast-growing nature of relevant research, our synthetic review is primarily based on the literature of the past 5 years (2014–2018), while building on several review and synthesis papers (e.g., Baker & Yacef, 2009; Baker & Siemens, 2014; Pardos, 2017).”
- Educational data mining and learning analytics: An updated survey (2020, background): “Ultimately, the differences between the two communities are more based on focus, research questions, and the eventual use of models, than on the methods used (Baker and Inventado, 2014).”
- Designing educational technologies in the age of AI: A learning sciences-driven approach (2019, methodology): “In addition, the data itself is becoming easier to use, thanks to some standardized data formats, and better analytical tools to manage and explore large datasets (Baker & Siemens, 2014, Karkalas & Mavrikis, 2016).”